Real-world datasets are inherently class-imbalanced, leading models to favor majority classes and struggle with minority ones. Existing logit adjustment methods address this by modifying logits based...
arXiv:2610.27443v1 Announce Type: new Abstract: Set-valued classification (SVC) offers a practical approach to communicating uncertainty while maintaining accuracy by predicting variable-sized sets p...
arXiv:2608.30699v1 Announce Type: cross Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to...
arXiv:2604. 02765v2 Announce Type: replace Abstract: Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular class-arrival scenarios underexplored.
arXiv:2609.16365v1 Announce Type: cross Abstract: Real-world datasets often exhibit long-tailed class distributions, where a few head classes contain a large number of training samples while a large...
arXiv:2602. 17853v2 Announce Type: replace Abstract: Logit adjustment corrects class imbalance using the empirical class prior.
arXiv:2609.16380v1 Announce Type: new Abstract: Class-balanced learning and label noise create a coupled failure mode: frequency correction prevents majority classes from dominating the decision rule...
arXiv:2409. 13007v3 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class.
arXiv:2607. 09832v1 Announce Type: new Abstract: Long-tailed recognition methods often modify losses, margins, or representations to reduce the dominance of frequent classes.
The paper introduces a collaborative optimization Boosting model for multiclass imbalanced learning that integrates density and confidence factors to create a noise‑resistant weight update mechanism and a dynamic sampling strategy. The modules are tightly coupled to coordinate weight updates, sample region partitioning, and region‑guided sampling. Experiments on 40 public imbalanced datasets show the model significantly outperforms seven state‑of‑the‑art baselines.
arXiv:2606. 11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of input data during model training.
VICAL is a framework for long‑tailed visual recognition that focuses on reducing prediction variance rather than increasing expert diversity. It combines Self‑Consistency Learning, which smooths the loss landscape and mitigates overfitting on tail classes, with Deep Ensemble Distillation, which encourages low‑frequency semantic agreement across experts. Experiments on CIFAR‑LT, ImageNet‑LT, and iNaturalist 2018 demonstrate that VICAL consistently outperforms state‑of‑the‑art methods.